This document discusses using a single channel EEG device to recognize emotions from EEG data. It collected data from 10 individuals labeled as stressed or relaxed. It preprocessed the raw EEG data using filters to isolate brain signals. It then used deep learning models including an LSTM network with and without attention to classify emotions. The LSTM with attention achieved 85% accuracy, which was an improvement over the LSTM without attention. Potential applications discussed include using EEG for stress reduction by customizing music, and emotion or word prediction. The document also discusses opportunities for future enhancements such as using convolutional layers or multi-modal networks incorporating additional physiological sensors.